Load SNP data

24 SNPs, for 70 individuals.

## $chr17.10159002
## 
##     0 0.020 0.021 0.025 0.031 0.034 0.037 0.042 0.052 0.053 0.058 0.068 
##    43     1     1     1     1     2     1     1     1     2     1     1 
## 0.069 0.082 0.087 0.089 0.122 0.129 0.233 0.531 0.543     1 
##     1     1     1     1     1     1     1     1     1     5 
## 
## $chr17.10159236
## 
##     0 0.002 0.003 0.368 0.378 0.379 0.502 0.631 0.702 0.776 0.785 0.791 
##    35     4     7     1     1     1     1     1     1     1     1     1 
## 0.834     1 1.626     2 
##     1    12     1     1 
## 
## $chr17.10160091
## 
##     0 0.000 0.195     1 1.000     2 
##    37    19     1    10     2     1 
## 
## $chr17.10160195
## 
##     0 0.002 0.003 0.004 0.345 0.346 0.518 0.740 0.997 0.999     1 1.003 
##    19     1     4     4     4     1     1     1     1     2    25     1 
## 1.440 1.835     2 
##     1     1     4 
## 
## $chr17.10160499
## 
##     0 0.002 0.003 0.022 0.026 0.078 0.082 0.090 0.091 0.097 0.098 0.120 
##    42     4     1     1     1     1     2     1     1     1     1     1 
## 0.144 0.154 0.160 0.165 0.176 0.222     1 
##     1     1     2     1     1     1     6 
## 
## $chr17.10160773
## 
##     0 0.000 0.462     1 1.000 
##    34    16     1    14     5 
## 
## $chr17.10161074
## 
##     0 0.000 0.461     1 1.000 
##    34    16     1    14     5 
## 
## $chr17.10161112
## 
##     0 0.000 0.339     1 1.000     2 
##    26    12     1    16     9     6 
## 
## $chr17.10161218
## 
##     0 0.014 0.017 0.021 0.025 0.027 0.030 0.031 0.042 0.045 0.053 0.057 
##    43     1     1     1     1     2     1     1     1     1     1     1 
## 0.060 0.072 0.076 0.090 0.098 0.122 0.219 0.375 0.468 0.473     1 
##     1     1     1     1     1     1     1     1     1     1     5 
## 
## $chr17.10161395
## 
##     0 0.002 0.003 0.004 0.460 0.994 0.997 0.998     1 1.000 
##    34     5    10     1     1     1     1     1    14     2 
## 
## $chr17.10161485
## 
##     0 0.002 0.003 0.004 0.055 0.076 0.090 0.322 0.935 0.972 0.987 0.988 
##    21     1     1     1     1     1     1     1     1     1     3     2 
## 0.989 0.991 0.995     1 1.082 1.357 1.974 1.975 1.977     2 
##     1     1     1    18     1     1     1     1     1     9 
## 
## $chr17.10162386
## 
##     0 0.002 0.004 0.040 0.054 0.076 0.090 0.342 0.956 0.982 0.991 0.992 
##    21     1     1     1     1     1     1     1     1     1     3     2 
## 0.993 0.994 0.999     1 1.086 1.574 1.982 1.983     2 
##     1     1     1    18     1     1     2     1     9 
## 
## $chr17.10162576
## 
##     0 0.002 0.003 0.024 0.033 0.042 0.067 0.713 0.970 0.975 0.976 0.979 
##    15     1     4     1     1     1     1     1     1     1     1     1 
## 0.984 0.991     1 1.000 1.003 1.093 1.796 1.976     2 
##     1     1    26     2     1     1     1     1     7 
## 
## $chr17.10162681
## 
##     0 0.000 0.379     1 1.000 
##    36    16     1    12     5 
## 
## $chr17.10162695
## 
##     0 0.002 0.378     1 1.000 
##    36    16     1    12     5 
## 
## $chr17.10162786
## 
##     0 0.000 0.302     1 1.000     2 2.000 
##    19     6     1    20    11     9     4 
## 
## $chr17.10162874
## 
##     0 0.000 0.376     1 1.000 
##    36    16     1    12     5 
## 
## $chr17.10162892
## 
##     0 0.002 0.003 0.004 0.027 0.029 0.150 0.180 0.200 0.245 0.326 0.336 
##    42     5     4     1     1     1     1     1     1     1     1     1 
## 0.425 0.438 0.457 0.808     1 
##     1     1     1     1     6 
## 
## $chr17.10163408
## 
##     0 0.003 0.338 0.797     1 1.001 1.004 1.006 1.041 
##    36    16     1     1    12     1     1     1     1 
## 
## $chr17.10163424
## 
##     0 0.002 0.003 0.310 0.786     1 1.000 1.001 1.034 
##    37     2    14     1     1    11     1     2     1 
## 
## $chr17.10163443
## 
##     0 0.002 0.003 0.304 0.782     1 1.000 1.001 1.033 
##    37     1    15     1     1    11     1     2     1 
## 
## $chr17.10163532
## 
##     0 0.062 0.063 0.111 0.120 0.132 0.157 0.158 0.568 0.783 0.987 0.991 
##    11     2     1     1     1     1     1     1     1     1     1     1 
## 0.992     1 1.012 1.036 1.038 1.049 1.076 1.083 1.515 1.896 1.897     2 
##     1    28     1     1     1     1     1     1     1     1     1     9 
## 
## $chr17.10163747
## 
##     0 0.000 0.002 0.209     1 1.000 
##    37    15     1     1    11     5 
## 
## $chr17.10163945
## 
##     0 0.000 0.361     1 1.000     2 
##    33    17     1    13     4     2

Read in phenotype data

Counts at 1024 bases, for 70 individuals.

## No id variables; using all as measure variables

Combining both

The goal of this project is to: - Identify base locations which contain significant effects between individual SNPs and the count data obtained from sequencing - Estimate the effect of the SNP value on the sequenced count data at each base

So, this exercise involves taking two SNPs, for example, and trying to plot 70 individuals’ worth of counts, and giving different colours depending on their SNP values. A fairly fruitless and noisy task.

## No id variables; using all as measure variables

## No id variables; using all as measure variables

A short guide to wavelet analysis on the sequenced count data. Let’s try and look at two individuals whose counts data are very different: one displays a lot of variation, the other does not: (note this is the raw wavelet count, NOT pre-processed to filter out low count WCs, or normalised against read counts, just to give a flavour of what wavelets on this data looks like).

##  [1] 21 16 24 32 30 50 28 61 63 12 40  7 54 11 34 49 70  6 62  1 19 48 52
## [24]  4 37 29  5 43 39 17 35 64 58  9 41 31 20 10 59 13 51 46 66 25  2 14
## [47]  3 18 56 22 57 67 42 38 65 36 47 44 26 68 45 27 60 15 33 55 53 23  8
## [70] 69

Individuals 21 (little variation) and 69 (a lot of variation)

## No id variables; using all as measure variables

##  [1] 0.7071068 0.5000000 0.3535534 0.2500000 0.2651650 0.1875000 0.1325825
##  [8] 0.0625000 0.1104854 0.0468750

##  [1] 2.121320 1.500000 1.944544 1.250000 1.856155 0.687500 1.679379
##  [8] 1.843750 1.458408 0.593750